| tag | b6dff4e3e2ab107c11a7a033b3d002e99de32be6 | |
|---|---|---|
| tagger | Kenneth Chan <kenneth@prediction.io> | Thu Jan 28 15:59:52 2016 -0800 |
| object | 3dce40022c465b74ad39ad836f4215ad27ffe0f9 |
3.1 release
| commit | 3dce40022c465b74ad39ad836f4215ad27ffe0f9 | [log] [tgz] |
|---|---|---|
| author | Kenneth Chan <kenneth@prediction.io> | Thu Jan 28 15:59:21 2016 -0800 |
| committer | Kenneth Chan <kenneth@prediction.io> | Thu Jan 28 15:59:21 2016 -0800 |
| tree | 0ed191c3a06d3f2af4b15807dc823680dd0c0944 | |
| parent | 55fd981c684d3d8744d9189d06da28e97b25a6ac [diff] |
Fix DataSource to read "content", "e-mail", and use label "spam" for tutorial data. Fix engine.json for default algorithm setting.
Look at the following tutorial for a Quick Start guide and implementation details.
Fix DataSource to read “content”, “e-mail”, and use label “spam” for tutorial data. Fix engine.json for default algorithm setting.
Modified PreparedData to use MLLib hashing and tf-idf implementations.
Fixed dot product implementation in the predict methods to work with batch predict method for evaluation.
Included three different data sets: e-mail spam, 20 newsgroups, and the rotten tomatoes semantic analysis set. Includes Multinomial Logistic Regression algorithm for text classification.
Fixed import script bug occuring with Python 2.
Changed data import Python script to pull straight from the 20 newsgroups page.